Method And Apparatus For Tracking Obstacle

ABSTRACT

The disclosure discloses a method and apparatus for tracking an obstacle. A specific embodiment of the method comprises: acquiring, in response to detecting multiple obstacle laser point clouds in a current laser point cloud frame, multiple historical obstacle laser point cloud sequences; calculating a respective association degree between each detected obstacle laser point cloud in the current laser point cloud frame and each historical obstacle laser point cloud sequence based on association information to obtain multiple association degrees; and searching for a historical obstacle laser point cloud sequence to which each detected obstacle laser point cloud in the current laser point cloud frame belongs based on the multiple association degrees.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority of Chinese Application No.201710835119.4, filed on Sep. 15, 2017, titled “Method and Apparatus forTacking Obstacle,” the entire disclosure of which is incorporated hereinby reference.

TECHNICAL FIELD

The disclosure relates to the field of vehicle, specifically to thefield of autonomous driving technology, and more specifically to amethod and apparatus for tracking an obstacle.

BACKGROUND

The autonomous driving vehicle makes its driving decision on the basisof sensing the driving environment. Tracking an obstacle is the mostpivotal process while sensing the driving environment. At present, acommonly used method for tracking an obstacle is: tracking the obstaclebased on the neighbouring relationship between laser point locations ofthe obstacle in a multi-frame laser point cloud.

The obstacle is tracked only based on the neighboring relationship of asingle location without considering the influence of other factors, suchas the laser point cloud of the obstacle changing over a view angle, andthe obstacle laser point detection error, during the obstacle trackingprocess, thereby leading to a low accuracy in the obstacle tracking.

SUMMARY

The disclosure provides a method and apparatus for tracking an obstacle,to solve the technical problems existing in the background part.

In a first aspect, the disclosure provides a method for tracking anobstacle, the method including: acquiring, in response to detectingmultiple obstacle laser point clouds in a current laser point cloudframe, multiple historical obstacle laser point cloud sequences, eachhistorical obstacle laser point cloud in a same historical obstaclelaser point cloud sequence representing a same obstacle, each historicalobstacle laser point cloud in the historical obstacle laser point cloudsequences being an obstacle laser point cloud in a historical laserpoint cloud frame collected prior to collecting the current laser pointcloud frame; calculating a respective association degree between eachdetected obstacle laser point cloud in the current laser point cloudframe and each historical obstacle laser point cloud sequence based onassociation information to obtain multiple association degrees, theassociation information including: a similarity degree between anappearance characteristic of the detected obstacle laser point cloud inthe current laser point cloud frame and an appearance characteristic ofthe historical obstacle laser point cloud in the historical obstaclelaser point cloud sequence, and consistency between a current motioncharacteristic of an obstacle represented by the detected obstacle laserpoint cloud in the current laser point cloud frame and a historicalmotion characteristic of an obstacle represented by the historicalobstacle laser point cloud in the historical obstacle laser point cloudsequence; and searching for a historical obstacle laser point cloudsequence to which each detected obstacle laser point cloud in thecurrent laser point cloud frame belongs based on the multipleassociation degrees.

In a second aspect, the disclosure provides an apparatus for tracking anobstacle, the apparatus including: a detection unit, configured foracquiring, in response to detecting multiple obstacle laser point cloudsin a current laser point cloud frame, multiple historical obstacle laserpoint cloud sequences, each historical obstacle laser point cloud in asame historical obstacle laser point cloud sequence representing a sameobstacle, each historical obstacle laser point cloud in the historicalobstacle laser point cloud sequences being an obstacle laser point cloudin a historical laser point cloud frame collected prior to collectingthe current laser point cloud frame; a calculation unit, configured forcalculating a respective association degree between each detectedobstacle laser point cloud in the current laser point cloud frame andeach historical obstacle laser point cloud sequence based on associationinformation to obtain multiple association degrees, the associationinformation including: a similarity degree between an appearancecharacteristic of the detected obstacle laser point cloud in the currentlaser point cloud frame and an appearance characteristic of thehistorical obstacle laser point cloud in the historical obstacle laserpoint cloud sequence, and consistency between a current motioncharacteristic of an obstacle represented by the detected obstacle laserpoint cloud in the current laser point cloud frame and a historicalmotion characteristic of an obstacle represented by the historicalobstacle laser point cloud in the historical obstacle laser point cloudsequence; and a matching unit, configured for searching for a historicalobstacle laser point cloud sequence to which each detected obstaclelaser point cloud in the current laser point cloud frame belongs basedon the multiple association degrees.

With the method and apparatus for tracking an obstacle according to thedisclosure, in response to detecting multiple obstacle laser pointclouds in a current laser point cloud frame, multiple historicalobstacle laser point cloud sequences are acquired, each historicalobstacle laser point cloud in a same historical obstacle laser pointcloud sequence representing a same obstacle, each historical obstaclelaser point cloud in the historical obstacle laser point cloud sequencesbeing an obstacle laser point cloud in a historical laser point cloudframe collected prior to collecting the current laser point cloud frame;a respective association degree between each detected obstacle laserpoint cloud in the current laser point cloud frame and each historicalobstacle laser point cloud sequence is calculated based on associationinformation to obtain multiple association degrees, the associationinformation including: a similarity degree between an appearancecharacteristic of the detected obstacle laser point cloud in the currentlaser point cloud frame and an appearance characteristic of thehistorical obstacle laser point cloud in the historical obstacle laserpoint cloud sequence, and consistency between a current motioncharacteristic of an obstacle represented by the detected obstacle laserpoint cloud in the current laser point cloud frame and a historicalmotion characteristic of an obstacle represented by the historicalobstacle laser point cloud in the historical obstacle laser point cloudsequence; and a historical obstacle laser point cloud sequence to whicheach detected obstacle laser point cloud in the current laser pointcloud frame belong is search for based on the multiple associationdegrees. An obstacle is tracked in real time considering the associationof the appearance characteristic and the motion characteristic of theobstacle with the process of tracking the obstacle, thereby improvingthe tracking accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

By reading and referring to detailed description on the non-limitingembodiments in the following accompanying drawings, other features,objects and advantages of the disclosure may become more apparent:

FIG. 1 shows a schematic diagram of a structure of hardware suitable foran autonomous vehicle according to the disclosure;

FIG. 2 shows a process diagram of an embodiment of a method for trackingan obstacle according to the disclosure; and

FIG. 3 shows a schematic diagram of a structure of an embodiment of anapparatus according to the disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

The present disclosure will be further described below in detail incombination with the accompanying drawings and the embodiments. Itshould be appreciated that the specific embodiments described herein aremerely used for explaining the relevant disclosure, rather than limitingthe disclosure. In addition, it should be noted that, for the ease ofdescription, only the parts related to the relevant disclosure are shownin the accompanying drawings.

It should also be noted that the embodiments in the present disclosureand the features in the embodiments may be combined with each other on anon-conflict basis. The present disclosure will be described below indetail with reference to the accompanying drawings and in combinationwith the embodiments.

Referring to FIG. 1, FIG. 1 shows a schematic diagram of a structure ofhardware suitable for an autonomous vehicle according to the disclosure.

As shown in FIG. 1, the autonomous vehicle includes a CPU 101, a memory102, a lidar 103, a GPS 104 and an inertial navigation system 105. TheCPU 101, the memory 102, the lidar 103, the GPS 104 and the inertialnavigation system 105 are connected to each other through a bus 106.

When the autonomous vehicle is running, the lidar therein may collectone frame of laser point cloud in each revolution. The laser emitted bythe lidar in one revolution is projected onto all obstacles around theautonomous vehicle, thereby forming laser points, which may form onelaser point cloud frame.

Referring to FIG. 2, FIG. 2 shows a process of an embodiment of a methodfor tracking an obstacle according to the disclosure. The method may beexecuted by an autonomous vehicle, for example, an autonomous vehiclehaving a structure as shown in FIG. 1. The method includes steps 201 to203.

Step 201 includes: acquiring, in response to detecting multiple obstaclelaser point clouds in a current laser point cloud frame, multiplehistorical obstacle laser point cloud sequences.

In the embodiment, a historical obstacle laser point cloud sequence mayinclude multiple historical obstacle laser point clouds, and historicalobstacle laser point clouds in a same historical obstacle laser pointcloud sequence correspond to a same obstacle. That is, a historicalobstacle laser point cloud sequence includes a series of obstacle laserpoint clouds which are associated with each other in time sequence andused for represent a same obstacle.

In the embodiment, the current laser point cloud frame does not refer toa specific laser point cloud frame collected by the lidar during onecollection period, i.e., during one revolution of the lidar. The currentlaser point cloud frame is relative to the historical laser point cloudframe collected prior to collecting the current laser point cloud frame.When a next laser point cloud frame after the current laser point cloudframe is collected, the current laser point cloud frame may also becomea historical laser point cloud frame relative to the next laser pointcloud frame. The order of historical obstacle laser point clouds in thehistorical obstacle laser point cloud sequence corresponds to the orderof collection periods corresponding to historical laser point cloudframes to which the historical laser point clouds belong. The collectionperiods corresponding to historical laser point cloud frames to whichtime-adjacent historical obstacle laser point clouds belong are alsoadjacent. The first historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence may belong to a lastlaser point cloud frame prior to the current laser point cloud frame.

In the embodiment, the current laser point cloud frame may be a latestcollected laser point cloud frame, and the historical obstacle laserpoint cloud in the historical obstacle laser point cloud sequence may bean obstacle laser point cloud in a historical laser point cloud framecollected before collecting the latest collected laser point cloudframe. The first historical obstacle laser point cloud in the historicalobstacle laser point cloud sequence may belong to a last laser pointcloud frame prior to the latest collected laser point cloud frame.

In the embodiment, first, multiple obstacle laser point clouds in thecurrent laser point cloud frame may be detected when tracking anobstacle. For example, multiple obstacle laser point clouds in thecurrent laser point cloud frame may be detected by segmenting thecurrent laser point cloud frame. Each obstacle laser point clouddetected in the current laser point cloud frame may represent arespective obstacle.

Step 202 includes: calculating an association degree between thedetected obstacle laser point cloud in the current laser point cloudframe and the historical obstacle laser point cloud sequence based onassociation information.

In the embodiment, after detecting multiple obstacle laser point cloudsin the current laser point cloud frame, and acquiring multiplehistorical obstacle laser point cloud sequences, an association degreebetween each detected obstacle laser point cloud in the current laserpoint cloud frame and each historical obstacle laser point cloudsequence may be calculated based on the association information.

In the embodiment, the association information may include, but is notlimited to: a similarity degree between an appearance characteristic ofthe detected obstacle laser point cloud in the current laser point cloudframe and an appearance characteristic of the historical obstacle laserpoint cloud in the historical obstacle laser point cloud sequence, andconsistency between a current motion characteristic of an obstaclerepresented by the detected obstacle laser point cloud in the currentlaser point cloud frame and a historical motion characteristic of anobstacle represented by the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence.

In the embodiment, the similarity degree between the appearancecharacteristic of the detected obstacle laser point cloud in the currentlaser point cloud frame and the appearance characteristic of thehistorical obstacle laser point cloud in the historical obstacle laserpoint cloud sequence may be used for representing appearance consistencybetween the detected obstacle laser point cloud in the current laserpoint cloud frame and the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence. The consistency betweenthe current motion characteristic of the obstacle represented by thedetected obstacle laser point cloud in the current laser point cloudframe and the historical motion characteristic of an obstaclerepresented by the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence may be used forrepresenting motion consistency between the detected obstacle laserpoint cloud in the current laser point cloud frame and the historicalobstacle laser point cloud in the historical obstacle laser point cloudsequence.

In the embodiment, the association degree between the detected obstaclelaser point cloud in the current laser point cloud frame and thehistorical obstacle laser point cloud sequence is calculated, and theappearance consistency and the motion consistency are considered in thecalculation.

Taking calculating an association degree between one detected obstaclelaser point cloud in the current laser point cloud frame and onehistorical obstacle laser point cloud sequence as an example, asimilarity degree between an appearance characteristic of the onedetected obstacle laser point cloud in the current laser point cloudframe and an appearance characteristic of the historical obstacle laserpoint cloud in the one historical obstacle laser point cloud sequence,and a parameter capable of representing consistency between a currentmotion characteristic of an obstacle represented by the one detectedobstacle laser point cloud in the current laser point cloud frame and ahistorical motion characteristic of an obstacle represented by thehistorical obstacle laser point clouds in the one historical obstaclelaser point cloud sequence may be calculated respectively. Then, thecalculated similarity degree and the parameter representing consistencymay be weighted and calculated to obtain the association degree betweenthe one detected obstacle laser point cloud in the current laser pointcloud frame and the one historical obstacle laser point cloud sequence.

In the embodiment, when calculating a similarity degree between anappearance characteristic of one detected obstacle laser point cloud inthe current laser point cloud frame and one obstacle laser point cloudin the historical obstacle laser point cloud sequence, a respectiveappearance similarity degree may be calculated for each appearancecharacteristic involved in the similarity degree calculation, and thenmultiple appearance similarity degrees may be weighted and calculated toobtain the similarity degree between the appearance characteristic ofthe one detected obstacle laser point cloud in the current laser pointcloud frame and the appearance characteristic of the one obstacle laserpoint cloud in the historical obstacle laser point cloud sequence.

In the embodiment, when calculating the similarity degree between theappearance characteristic of the one detected obstacle laser point cloudin the current laser point cloud frame and the appearance characteristicof the historical obstacle laser point cloud in the one historicalobstacle laser point cloud sequence, only the appearance characteristicof the one detected obstacle laser point cloud in the current laserpoint cloud frame and an appearance characteristic of one obstacle laserpoint cloud in the one historical obstacle laser point cloud sequencemay be calculated. For example, only a similarity degree between theappearance characteristic of the one detected obstacle laser point cloudin the current laser point cloud frame and the appearance characteristicof the obstacle laser point cloud of the historical obstacle laser pointcloud sequence in a last laser point cloud frame prior to the currentlaser point cloud frame is calculated, and this similarity degree isused as the similarity degree between the appearance characteristic ofthe one detected obstacle laser point cloud in the current laser pointcloud frame and the appearance characteristic of the historical obstaclelaser point cloud in the one historical obstacle laser point cloudsequence. Alternatively, similarity degrees between the appearancecharacteristic of the one detected obstacle laser point cloud in thecurrent laser point cloud frame and appearance characteristics ofmultiple historical obstacle laser point clouds in the one historicalobstacle laser point cloud sequence may be calculated, and then anaverage value or a median of the calculated similarity degrees may beused as the similarity degree between the appearance characteristic ofthe one detected obstacle laser point cloud in the current laser pointcloud frame and the appearance characteristics of the historicalobstacle laser point clouds in the one historical obstacle laser pointcloud sequence.

In the embodiment, the current motion characteristic may be a motioncharacteristic of an obstacle represented by an obstacle laser pointcloud detected at a moment of collecting the current laser point cloudframe, for example, a speed and a posture of the obstacle represented bythe obstacle laser point cloud detected at the moment of collecting thecurrent laser point cloud frame. The historical motion characteristicmay be a historical motion characteristic of an obstacle represented bya historical obstacle laser point cloud at a moment of collecting thehistorical laser point cloud frame to which the historical obstaclelaser point cloud in the historical obstacle laser point cloud sequencebelongs, for example, a historical speed and a historical posture of theobstacle represented by the historical obstacle laser point cloud at themoment of collecting the historical laser point cloud frame to which thehistorical obstacle laser point cloud in the historical obstacle laserpoint cloud sequence belongs.

The consistency between the current motion characteristic of theobstacle represented by the detected obstacle laser point cloud in thecurrent laser point cloud frame and the historical motion characteristicof the obstacle represented by the historical obstacle laser point cloudin the historical obstacle laser point cloud sequence may be representedby a parameter representing consistency between the current motioncharacteristic of the obstacle represented by the detected obstaclelaser point cloud in the current laser point cloud frame and thehistorical motion characteristic of the obstacle represented by multiplehistorical obstacle laser point clouds in the historical obstacle laserpoint cloud sequence.

For example, taking the speed in the motion characteristics as anexample, the speed is a vector including a speed magnitude and a speeddirection. The speed of the obstacle represented by the obstacle laserpoint cloud in the current laser point cloud frame detected at themoment that the current laser point cloud frame is collected may berepresented by a dot in a two-dimensional coordinate system includingcoordinate axes corresponding to the speed magnitude and direction.Historical speeds of the obstacle represented by the historical obstaclelaser point cloud at moments that the historical laser point cloudframes of historical obstacle laser point clouds in the historicalobstacle laser point cloud sequence are respectively collected each mayalso be represented by one dot, and then all of the dots may be fittedto obtain a current fitting result. Then, a deviation value between thecurrent fitting result and a historical fitting result obtained by onlyfitting the dots representing the historical speeds of the obstaclerepresented by the historical obstacle laser point cloud may becalculated. The deviation value may be a deviation angle between a slopeof a fitting line corresponding to the current fitting result and aslope of a fitting line corresponding to the historical fitting result.The deviation value may be used as a parameter representing consistencybetween motion characteristics, to measure the consistency between themotion characteristics.

In the embodiment, after calculating the association degree between eachdetected obstacle laser point cloud in the current laser point cloudframe and each historical obstacle laser point cloud sequence, multipleassociation degrees may be obtained.

For example, when the number of detected obstacle laser point clouds inthe current laser point cloud frame is M, and the number of acquiredhistorical obstacle laser point cloud sequences is N, then M*Nassociation degrees can be calculated.

In some optional implementations of the embodiment, the appearancecharacteristic includes: a size, the number of the laser point, a laserpoint density and a geometrical shape. A size of the obstacle laserpoint cloud may be a size of a bounding box of the obstacle laser pointcloud.

The similarity degree between a size of the detected obstacle laserpoint cloud in the current laser point cloud frame and a size of thehistorical obstacle laser point cloud in the historical obstacle laserpoint cloud sequence may be used for representing consistency betweenthe size of the detected obstacle laser point cloud in the current laserpoint cloud frame and the size of the historical obstacle laser pointcloud in the historical obstacle laser point cloud sequence. Thesimilarity degree between the number of the laser point of the detectedobstacle laser point cloud in the current laser point cloud frame andthe number of the laser point of the historical obstacle laser pointcloud in the historical obstacle laser point cloud sequence may be usedfor representing consistency between the number of the laser point ofthe detected obstacle laser point cloud in the current laser point cloudframe and the number of the laser point of the historical obstacle laserpoint cloud in the historical obstacle laser point cloud sequence. Thesimilarity degree between a geometrical shape of the detected obstaclelaser point cloud in the current laser point cloud frame and ageometrical shape of the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence may be used forrepresenting consistency between the geometrical shape of the detectedobstacle laser point cloud in the current laser point cloud frame andthe geometrical shape of the historical obstacle laser point cloud inthe historical obstacle laser point cloud sequence.

When calculating the similarity degree between an appearancecharacteristic of one detected obstacle laser point cloud in the currentlaser point cloud frame and an appearance characteristic of one obstaclelaser point cloud in the historical obstacle laser point cloud sequence,an appearance similarity degree may be calculated for each of appearancecharacteristics, such as size, the number of the laser point, laserpoint density or geometrical shape, and then multiple appearancesimilarity degrees are weighted and calculated to obtain the similaritydegree between the appearance characteristic of the one detectedobstacle laser point cloud in the current laser point cloud frame andthe appearance characteristic of the one obstacle laser point cloud inthe historical obstacle laser point cloud sequence.

When calculating a similarity degree between a size of one detectedobstacle laser point cloud in the current laser point cloud frame and asize of one historical obstacle laser point cloud in the historicalobstacle laser point cloud sequence, first, a length, a width and aheight of a bounding box of the one detected obstacle laser point cloudin the current laser point cloud frame, and a length, a width and aheight of a bounding box of the one historical obstacle laser pointcloud in the historical obstacle laser point cloud sequence may bedetermined respectively based on postures of the bounding box of the onedetected obstacle laser point cloud in the current laser point cloudframe and the bounding box of the one historical obstacle laser pointcloud in the historical obstacle laser point cloud sequence i.e.,deflection angles of the bounding box in the X axis, the Y axis and theZ axis. Then, a difference value of the lengths, a difference value ofthe widths, and a difference value of the heights may be calculated. Forexample, a difference value of the lengths may be represented by (length1−length 2)/max(length 1, length 2). The length 1 may represent thelength of the bounding box of the one detected obstacle laser pointcloud in the current laser point cloud frame or the length of thebounding box of the one historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence, and the length 2 mayrepresent the length of another bounding box. The difference value ofthe lengths, the difference value of the widths, and the differencevalue of the heights may be calculated respectively, and a maximumvalue, a minimum value and an average value of all difference values maybe selected for calculating the similarity degree between the size ofthe one detected obstacle laser point cloud in the current laser pointcloud frame and the size of the one historical obstacle laser pointcloud in the historical obstacle laser point cloud sequence.

When calculating a similarity degree between the number of a laser pointof one detected obstacle laser point cloud in the current laser pointcloud frame and the number of a laser point of one historical obstaclelaser point cloud in the historical obstacle laser point cloud sequence,the similarity degree may be represented by (the first number of thelaser point—the second number of the laser point)/max(the first numberof the laser point, the second number of the laser point). The firstnumber of the laser point may represent the number of the laser point ofthe one detected obstacle laser point cloud in the current laser pointcloud frame or the number of the laser point of the one historicalobstacle laser point cloud in the historical obstacle laser point cloudsequence, and the second number of the laser point may represent anotherlaser point count.

When calculating a similarity degree between a geometrical shape of onedetected obstacle laser point cloud in the current laser point cloudframe and a geometrical shape of one historical obstacle laser pointcloud in the historical obstacle laser point cloud sequence, thesimilarity degree between a laser point distribution characteristic ofthe one detected obstacle laser point cloud in the current laser pointcloud frame and a laser point distribution characteristic of the onehistorical obstacle laser point cloud in the historical obstacle laserpoint cloud sequence may be used as a geometrical shape similaritydegree. For the one obstacle laser point cloud, the space occupied bythe bounding box of the obstacle laser point cloud may be divided into3D grids of equal volume, statistics of the number of the laser point ofthe obstacle laser point cloud in each of the 3D grids is performed, anda distribution eigenvector whose number of dimensions is identical tothe number of 3D grids is generated. The value of each dimension in thedistribution eigenvector is a normalized value corresponding to thenumber of the laser point in the obstacle laser point cloud of the 3Dgrid corresponding to the dimension, and the distribution characteristicof the obstacle laser point cloud may be represented by the distributioneigenvector. A similarity degree between the distribution eigenvectorrepresenting the laser point distribution characteristic of the onedetected obstacle laser point cloud in the current laser point cloudframe and a distribution eigenvector representing the laser pointdistribution characteristic of the one historical obstacle laser pointcloud in the historical obstacle laser point cloud sequence may becalculated.

In some optional implementations of the embodiment, a current motioncharacteristic of the obstacle represented by the detected obstaclelaser point cloud in the current laser point cloud frame may include,but is not limited to: an observed speed of the obstacle represented bythe detected obstacle laser point cloud in the current laser point cloudframe at the moment of collecting the current laser point cloud frame,and an observed posture of the obstacle represented by the detectedobstacle laser point cloud in the current laser point cloud frame at themoment of collecting the current laser point cloud frame. The historicalmotion characteristic of the obstacle represented by the historicalobstacle laser point cloud in the historical obstacle laser point cloudsequence may include, but is not limited to: a historical speed obtainedthrough a motion estimation of the obstacle represented by thehistorical obstacle laser point cloud at the moment of collecting thehistorical laser point cloud frame to which the historical obstaclelaser point cloud in the historical obstacle laser point cloud sequencebelongs, and a historical posture of the obstacle represented by thehistorical obstacle laser point cloud at the moment of collecting thehistorical laser point cloud frame to which the historical obstaclelaser point cloud in the historical obstacle laser point cloud sequencebelongs. The filtering method used for the motion estimation mayinclude, but is not limited to: Gaussian Filter, Kalman Filter, ExtendedKalman Filter and Unscented Kalman Filter.

When calculating consistency between a current motion characteristic ofan obstacle represented by one detected obstacle laser point cloud inthe current laser point cloud frame at the moment of collecting thecurrent laser point cloud frame and a historical motion characteristicof an obstacle represented by a historical obstacle laser point cloud ata moment of collecting a historical laser point cloud frame to which thehistorical obstacle laser point cloud in the historical obstacle laserpoint cloud sequence belongs, an observed speed of the obstaclerepresented by the one detected obstacle laser point cloud in thecurrent laser point cloud frame at the moment of collecting the currentlaser point cloud frame may be first calculated. When calculating theobserved speed, the observed speed may be obtained by dividing adistance between an interest point of the one detected obstacle laserpoint cloud in the current laser point cloud frame at the moment thatthe current laser point cloud frame is collected and an interest pointof a first historical obstacle laser point cloud in the historicalobstacle laser point cloud sequence by a length of a collection periodof the lidar, i.e., a length of time of collecting a laser point cloudframe by the lidar in one revolution. The interest point may include,but is not limited to: a center point of the bounding box, a gravitycenter point of the bounding box, an edge point of the bounding box andan angular point of the bounding box.

When calculating consistency between a current motion characteristic ofan obstacle represented by one detected obstacle laser point cloud inthe current laser point cloud frame and a historical motioncharacteristic of an obstacle represented by a historical obstacle laserpoint cloud in one historical obstacle laser point cloud sequence, aparameter capable of representing consistency between an observed speedof the obstacle represented by the one detected obstacle laser pointcloud in the current laser point cloud frame and a historical speedobtained through a motion estimation of the obstacle represented by thehistorical obstacle laser point cloud in the one historical obstaclelaser point cloud sequence and a parameter capable of representingconsistency between an observed posture of the obstacle represented bythe one detected obstacle laser point cloud in the current laser pointcloud frame and a historical posture of the obstacle represented by thehistorical obstacle laser point cloud in the one historical obstaclelaser point cloud sequence may be calculated respectively. Then, theparameter representing speed consistency and the parameter representingposture consistency may be weighted and calculated to obtain a parametercapable of representing consistency between the current motioncharacteristic of the obstacle represented by the one detected obstaclelaser point cloud in the current laser point cloud frame and thehistorical motion characteristic of the obstacle represented by thehistorical obstacle laser point cloud in the one historical obstaclelaser point cloud sequence.

When calculating a parameter representing consistency between anobserved speed of an obstacle represented by one detected obstacle laserpoint cloud in the current laser point cloud frame at the moment ofcollecting the current laser point cloud frame and a historical speedobtained through a motion estimation of an obstacle represented by ahistorical obstacle laser point cloud in one historical obstacle laserpoint cloud sequence at a moment of collecting a historical laser pointcloud frame to which the historical obstacle laser point cloud in theone historical obstacle laser point cloud sequence belongs, a modulus ofa vector difference between a vector of the observed speed of theobstacle represented by the one detected obstacle laser point cloud inthe current laser point cloud frame at the moment that the current laserpoint cloud frame is collected and a vector of the historical speedobtained through the motion estimation of the obstacle represented bythe historical obstacle laser point cloud in the one historical obstaclelaser point cloud sequence at the moment that the last laser point cloudframe prior to the current laser point cloud frame is collected, i.e., ahistorical laser point cloud frame to which a first historical obstaclelaser point cloud in the one historical obstacle laser point cloudsequence belongs may be calculated, and the calculated modulus of thevector difference is used as the parameter representing consistencybetween the observed speed of the obstacle represented by the onedetected obstacle laser point cloud in the current laser point cloudframe at the moment that the current laser point cloud frame iscollected and the historical speed obtained through the motionestimation of the obstacle represented by the historical obstacle laserpoint cloud in the one historical obstacle laser point cloud sequence atthe moment that the historical laser point cloud frame to which thehistorical obstacle laser point cloud in the historical obstacle laserpoint cloud sequence belongs is collected.

When calculating a parameter representing consistency between anobserved speed of an obstacle represented by one detected obstacle laserpoint cloud in the current laser point cloud frame at the moment ofcollecting the current laser point cloud frame and a historical speedobtained through a motion estimation of an obstacle represented by ahistorical obstacle laser point cloud in one historical obstacle laserpoint cloud sequence at a moment of collecting the historical laserpoint cloud frame to which the historical obstacle laser point cloud inthe one historical obstacle laser point cloud sequence belongs, moduliof vector differences between a vector of the observed speed of theobstacle represented by the one detected obstacle laser point cloud inthe current laser point cloud frame at the moment of collecting thecurrent laser point cloud frame and vectors of historical speedsobtained through a motion estimation of the obstacle represented by thehistorical obstacle laser point clouds in the one historical obstaclelaser point cloud sequence at moments of collecting respectivehistorical obstacle laser frames of multiple historical obstacle laserpoint clouds in the one historical obstacle laser point cloud sequencemay be respectively calculated, and an average value of the moduli ofthe vector differences is used as the parameter representing consistencybetween the observed speed of the obstacle represented by the onedetected obstacle laser point cloud in the current laser point cloudframe at the moment of collecting the current laser point cloud frameand the historical speed obtained through the motion estimation of theobstacle represented by the historical obstacle laser point cloud in theone historical obstacle laser point cloud sequence at the moment ofcollecting the historical laser point cloud frame to which thehistorical obstacle laser point cloud in the historical obstacle laserpoint cloud sequence belongs.

Step 203 includes: searching for a historical obstacle laser point cloudsequence to which the detected obstacle laser point cloud belongs.

In the embodiment, after obtaining multiple association degrees bycalculating an association degree between each detected obstacle laserpoint cloud in the current laser point cloud frame and each historicalobstacle laser point cloud sequence, a historical obstacle laser pointcloud sequence to which the each detected obstacle laser point cloud inthe current laser point cloud frame belongs may be searched for based onthe association degrees obtained in the step 202.

For example, when an association degree between one detected obstaclelaser point cloud in the current laser point cloud frame and onehistorical obstacle laser point cloud sequence is greater than anassociation degree threshold and the association degree between the onedetected obstacle laser point cloud in the current laser point cloudframe and the one historical obstacle laser point cloud sequence isgreater than association degrees between the one detected obstacle laserpoint cloud in the current laser point cloud frame and other historicalobstacle laser point cloud sequences in the multiple historical obstaclelaser point cloud sequences, the one historical obstacle laser pointcloud sequence may be used as a candidate historical obstacle laserpoint cloud sequence of the one detected obstacle laser point cloud.When the one historical obstacle laser point cloud sequence is only usedas the candidate historical obstacle laser point cloud of the onedetected obstacle laser point cloud in the current laser point cloudframe, the one historical obstacle laser point cloud sequence may beused as the historical obstacle laser point cloud sequence to which theone detected obstacle laser point cloud in the current laser point cloudframe belongs, the one detected obstacle laser point cloud in thecurrent laser point cloud frame may be added to the historical obstaclelaser point cloud sequence to which the detected obstacle laser pointcloud belongs as a historical obstacle laser point cloud, and thehistorical obstacle laser point cloud is not involved in matching anymore. When the one historical obstacle laser point cloud sequence isused as a candidate historical obstacle laser point cloud of multiple,e.g., two detected obstacle laser point clouds in the current laserpoint cloud frame, the one historical obstacle laser point cloudsequence may be used as the historical obstacle laser point cloudsequence to which the detected obstacle laser point cloud whoseassociation degree is higher belongs, and the one historical obstaclelaser point cloud sequence is not involved in matching any more. Forother unsuccessfully matched detected obstacle laser point clouds, acorresponding historical obstacle sequence whose association degreesecond highest and greater than an association degree threshold may beused as a new corresponding historical obstacle laser point cloudsequence having a highest association degree, and the searching processis repeated, by that analogy, until all searching processes arefinished. When a detected obstacle laser point cloud fails to match anyhistorical obstacle laser point cloud sequence, a historical obstaclelaser point cloud sequence including the detected obstacle laser pointcloud may be established for use in subsequent obstacle tracing process.

The detected obstacle laser point cloud in the current laser point cloudframe and the found historical obstacle laser point cloud sequence towhich the detected obstacle laser point cloud belongs represent a sameobstacle. Therefore, after finding out the historical obstacle laserpoint cloud sequence to which the detected obstacle laser point cloudbelongs in the current laser point cloud frame, a location and a postureof the detected obstacle laser point cloud in the current laser pointcloud frame may be used as a latest location and a latest posture of thesame obstacle represented by the detected obstacle laser point cloud,the location of the obstacle laser point cloud may be a location of acenter point of a bounding box of the obstacle laser point cloud, andthe posture of the obstacle laser point cloud may be a posture of thebounding box of the obstacle laser point cloud. Thus, the latestlocation and a latest speed of the represented same obstacle may bedetermined to achieve tracking the represented same obstacle in realtime.

In some optional implementations of the embodiment, a bipartite graphmay be first established when searching for a historical obstacle laserpoint cloud sequence to which each detected obstacle laser point cloudin the current laser point cloud frame belongs based on the multipleassociation degrees. In the bipartite graph, each detected obstaclelaser point cloud in the current laser point cloud frame corresponds toone node and each of the multiple historical obstacle laser point cloudsequences corresponds to one node in the bipartite graph.

In the bipartite graph, if an association degree between one detectedobstacle laser point cloud in the current laser point cloud frame andone historical obstacle laser point cloud sequence is greater than anassociation degree threshold, then a node representing the one detectedobstacle laser point cloud in the current laser point cloud frame and anode representing the one historical obstacle laser point cloud sequenceare connected by a line segment, and a weight of the line segment is theassociation degree between the one detected obstacle laser point cloudin the current laser point cloud frame and the one historical obstaclelaser point cloud sequence.

It should be appreciated that the steps 201-203 may be executed at atime interval of a collection period of the lidar, i.e., a time intervalof a revolution of the lidar. That is, the steps 201-203 may be executedin response to collecting a latest laser point cloud frame. Acquiredmultiple historical obstacle laser point cloud sequences may refer toall historical obstacle laser point cloud sequences formed by trackingan obstacle prior to collecting the current laser point cloud frame.Therefore, the number of the multiple historical obstacle laser pointcloud sequences acquired during executing the step 201 may be differentfrom the number of the multiple historical obstacle laser point cloudsequences acquired during executing the step 201 last time.

For example, after executing the steps 201-203, when scanning a newobstacle, a new historical obstacle laser point cloud sequence may beestablished, and if it is determined that a period of an obstaclerepresented by a historical obstacle laser point cloud in a historicalobstacle laser point cloud sequence being located outside a scanningrange of the lidar reaches a time threshold, the historical obstaclelaser point cloud sequence may be deleted.

After establishing the bipartite graph, the bipartite graph may bedivided into multiple sub-bipartite graphs using a graph traversalmethod. Then, solutions of the sub-bipartitie graphs may be successivelyacquired. When a solution of a sub-bipartite graph is acquired, anappropriate operation may be executed based on a node characteristic ofthe sub-bipartite graph.

When a sub-bipartite graph includes a node representing the detectedobstacle laser point cloud in the current laser point cloud frame anddoes not include a node representing the historical obstacle laser pointcloud sequence, a historical obstacle laser point cloud sequenceincluding the detected obstacle laser point cloud in the current laserpoint cloud frame may be established, the detected obstacle laser pointcloud in the current laser point cloud frame may be added to theestablished historical obstacle laser point cloud sequence as ahistorical obstacle laser point cloud, i.e., a first historical obstaclelaser point cloud in the historical obstacle laser point cloud sequence.

For example, when an obstacle represented by the detected obstacle laserpoint cloud in the current laser point cloud frame is an emergingobstacle, a historical obstacle laser point cloud sequence in which thedetected obstacle laser point cloud in the current laser point cloudframe is a first historical obstacle laser point cloud may beestablished, and the established historical obstacle laser point cloudsequence may be used for tracking the emerging obstacle when a nextlaser point cloud frame after the current laser point cloud frame iscollected.

When the sub-bipartite graph includes the node representing thehistorical obstacle laser point cloud sequence and does not include thenode representing the detected obstacle laser point cloud in the currentlaser point cloud frame, for example, when an obstacle represented bythe historical obstacle laser point cloud sequence is not located in ascanning range of the lidar, the historical obstacle laser point cloudsequence may be marked as unmatching any detected obstacle laser pointcloud in the current laser point cloud frame in this matching process,and the number of unmatched times of the historical obstacle laser pointcloud sequence may be updated, i.e., the number of unmatched times ofthe historical obstacle laser point cloud sequence may be increased by1.

When the sub-bipartite graph includes the node representing the detectedobstacle laser point cloud in the current laser point cloud frame, andthe node representing the historical obstacle laser point cloudsequence, i.e., the detected obstacle laser point cloud in the currentlaser point cloud frame corresponding to each node representing thedetected obstacle laser point cloud in the sub-bipartite graph isassociated with at least one historical obstacle laser point cloudsequence, a maximum match of the sub-bipartite graph may be calculatedusing a Hungarian algorithm to obtain a matching result. Each matchingresult includes: a detected obstacle laser point cloud in the currentlaser point cloud frame and a historical obstacle laser point cloudsequence to which the detected obstacle laser point cloud belongs. Thedetected obstacle laser point cloud in the current laser point cloudframe in the matching result may be added to a historical obstacle laserpoint cloud sequence of the matching result as a first historicalobstacle laser point cloud in the historical obstacle laser point cloudsequence.

For the detected obstacle laser point cloud in the current laser pointcloud frame in the matching result, a speed may be obtained through amotion estimation of the detected obstacle laser point cloud in thecurrent laser point cloud frame using a preset filtering algorithm. Thepreset filtering algorithm may include, but is not limited to: GaussianFilter, Kalman Filter, Extended Kalman Filter and Unscented KalmanFilter.

The detected obstacle laser point cloud in the current laser point cloudframe becomes the first historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence to which the detectedobstacle laser point cloud belongs. Therefore, the speed obtainedthrough the motion estimation can be used for tracking the obstacle whena next laser point cloud frame after the current laser point cloud frameis collected.

After calculating the maximum match of the sub-bipartite graph, for ahistorical obstacle laser point cloud sequence without a correspondingmatching result, i.e., a historical obstacle laser point cloud sequencewithout an established subordination relationship with any detectedobstacle laser point cloud in the current laser point cloud frame, thenumber of unmatched times of the historical obstacle laser point cloudsequence without the corresponding matching result may be updated, i.e.,the number of unmatched times is increased by 1, and the historicalobstacle laser point cloud sequence may be marked as unmatching anydetected obstacle laser point cloud in the current laser point cloudframe in this matching process.

After successively acquiring solutions of all sub-bipartite graphs inthe bipartite graph, one tracking of the obstacle may be completed.

By converting the process of searching for the historical obstacle laserpoint cloud sequence to which each detected obstacle laser point cloudin the current laser point cloud frame belongs into the process ofacquiring a solution of each sub-bipartite graph, and only nodes havingcertain association degrees are involved in calculation in the processof acquiring the solution of each sub-bipartite graph, thereby greatlyreducing the calculation of the whole searching process, i.e., greatlyreducing the calculation of the tracking process.

In some optional implementations of the embodiment, after successivelyacquiring solutions of all sub-bipartite graphs in the bipartite graph,whether the multiple historical obstacle laser point cloud sequencesinclude a historical obstacle laser point cloud sequence meeting atleast one preset condition may be further determined. The presetcondition includes: a number of unmatched times within a preset timeperiod being greater than a number threshold, a ratio of the number ofunmatched times to a sum of a number of matching times and the number ofunmatched times within the preset time period being greater than a ratiothreshold, a number of consecutively unmatched times within the presettime period being greater than the number threshold, and a ratio of thenumber of consecutively unmatched times to the number of unmatched timeswithin the preset time period being greater than the ratio threshold.When the multiple historical obstacle laser point cloud sequencesinclude the historical obstacle laser point cloud sequence meeting thepreset condition, the historical obstacle laser point cloud sequencemeeting the preset condition may be deleted.

When the historical obstacle laser point cloud sequence meets the atleast one preset condition, an obstacle represented by a historicalobstacle laser point cloud in the historical obstacle laser point cloudsequence is likely to be located outside the scanning range of thelidar, and the historical obstacle laser point cloud sequence may bedeleted. In subsequent tracking, the historical obstacle laser pointcloud sequence is not involved in matching any more.

Further referring to FIG. 3, as implementations of the methods shown inthe above figures, the disclosure provides an embodiment of an apparatusfor tracking an obstacle. The embodiment corresponds to the embodimentof the method shown in FIG. 2.

As shown in FIG. 3, the apparatus for tracking an obstacle includes: adetection unit 301, a calculation unit 302, and a matching unit 303. Thedetection unit 301 is configured for acquiring, in response to detectingmultiple obstacle laser point clouds in a current laser point cloudframe, multiple historical obstacle laser point cloud sequences, eachhistorical obstacle laser point cloud in a same historical obstaclelaser point cloud sequence representing a same obstacle, each historicalobstacle laser point cloud in the historical obstacle laser point cloudsequences being an obstacle laser point cloud in a historical laserpoint cloud frame collected prior to collecting the current laser pointcloud frame; the calculation unit 302 is configured for calculating arespective association degree between each detected obstacle laser pointcloud in the current laser point cloud frame and each historicalobstacle laser point cloud sequence based on association information toobtain multiple association degrees, the association informationincluding: a similarity degree between an appearance characteristic ofthe detected obstacle laser point cloud in the current laser point cloudframe and an appearance characteristic of the historical obstacle laserpoint cloud in the historical obstacle laser point cloud sequence, andconsistency between a current motion characteristic of an obstaclerepresented by the detected obstacle laser point cloud in the currentlaser point cloud frame and a historical motion characteristic of anobstacle represented by the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence; and the matching unit303 is configured for searching for a historical obstacle laser pointcloud sequence to which each detected obstacle laser point cloud in thecurrent laser point cloud frame belongs based on the multipleassociation degrees.

In some optional implementations of the embodiment, the appearancecharacteristic includes: a size, a number of the laser point, a laserpoint density and a geometrical shape.

In some optional implementations of the embodiment, the current motioncharacteristic of the obstacle represented by the detected obstaclelaser point cloud in the current laser point cloud frame includes: anobserved speed of the obstacle represented by the detected obstaclelaser point cloud at a moment of collecting the current laser pointcloud frame, and an observed posture of the obstacle represented by thedetected obstacle laser point cloud at the moment of collecting thecurrent laser point cloud frame, and the historical motioncharacteristic of the obstacle represented by the historical obstaclelaser point cloud in the historical obstacle laser point cloud sequenceincludes: a historical speed obtained through a motion estimation of theobstacle represented by the historical obstacle laser point cloud at amoment of collecting the historical laser point cloud frame to which thehistorical obstacle laser point cloud in the historical obstacle laserpoint cloud sequence belongs, and a historical posture of the obstaclerepresented by the historical obstacle laser point cloud at the momentof collecting the historical laser point cloud frame to which thehistorical obstacle laser point cloud in the historical obstacle laserpoint cloud sequence belongs.

In some optional implementations of the embodiment, the matching unitincludes: a bipartite graph matching subunit, configured forestablishing a bipartite graph, where each detected obstacle laser pointcloud in the current laser point cloud frame corresponds to one node andeach historical obstacle laser point cloud sequence in the multiplehistorical obstacle laser point cloud sequences correspond to one nodein the bipartite graph, and for a pair of the detected obstacle laserpoint cloud in the current laser point cloud frame and the historicalobstacle laser point cloud sequence whose association degree greaterthan an association degree threshold, a weight of a line segment betweena node corresponding to the detected obstacle laser point cloud in thecurrent laser point cloud frame and a node corresponding to thehistorical obstacle laser point cloud sequence in the bipartite graph isthe association degree; and searching for the historical obstacle laserpoint cloud sequence to which the each detected obstacle laser pointcloud in the current laser point cloud frame belongs based on thebipartite graph.

In some optional implementations of the embodiment, the bipartite graphmatching subunit is further configured for: dividing the bipartite graphinto multiple sub-bipartite graphs using a graph traversal method;acquiring a solution for each of the sub-bipartite graphs by:establishing a historical obstacle laser point cloud sequence includingthe detected obstacle laser point cloud in the current laser point cloudframe when the each sub-bipartite graph includes a node representing thedetected obstacle laser point cloud in the current laser point cloudframe and does not include a node representing the historical obstaclelaser point cloud sequence; updating a number of unmatched times of thehistorical obstacle laser point cloud sequence when the eachsub-bipartite graph includes the node representing the historicalobstacle laser point cloud sequence and does not include the noderepresenting the detected obstacle laser point cloud in the currentlaser point cloud frame; and calculating a maximum match of the eachsub-bipartite graph using a Hungarian algorithm to obtain a matchingresult when each sub-bipartite graph includes the node representing thedetected obstacle laser point cloud in the current laser point cloudframe, and includes the node representing the historical obstacle laserpoint cloud sequence; adding the detected obstacle laser point cloud inthe current laser point cloud frame in the matching result to thehistorical obstacle laser point cloud sequence in the matching result,obtaining a speed of the obstacle represented by the detected obstaclelaser point cloud through using a preset filtering algorithm to performa motion estimation of the obstacle represented by the detected obstaclelaser point cloud in the current laser point cloud frame in the matchingresult, and updating a number of unmatched times of the historicalobstacle laser point cloud sequence non-corresponding to the matchingresult.

In some optional implementations of the embodiment, the apparatus fortracking an obstacle further includes: a determining unit, configuredfor determining whether multiple historical obstacle laser point cloudsequences comprises a historical obstacle laser point cloud sequencemeeting at least one preset condition, the preset condition including: anumber of unmatched times within a preset time length being greater thana number threshold, a ratio of the number of unmatched times to a sum ofa number of matching times and the number of unmatched times within thepreset time length being greater than a ratio threshold, a number ofconsecutively unmatched times within the preset time length beinggreater than the number threshold, and a ratio of a number ofconsecutively unmatched times to the number of unmatched times withinthe preset time length being greater than the ratio threshold; anddeleting the historical obstacle laser point cloud sequence meeting theat least one preset condition, when the multiple historical obstaclelaser point cloud sequences comprises the historical obstacle laserpoint cloud sequence meeting at least one preset condition.

The disclosure further provides an autonomous vehicle, which may beequipped with one or more processors; and a memory for storing one ormore programs, where the one or more programs may include instructionsfor executing the operations according to the steps 201-203. The one ormore programs, when executed by the one or more processors, cause theone or more processors to execute the operations according to the steps201-203.

The disclosure further provides a computer-readable medium. Thecomputer-readable medium may be the computer-readable medium included inthe autonomous vehicle or a stand-alone computer-readable medium notassembled into the autonomous vehicle. The computer-readable mediumstores one or more programs, and the one or more programs, when executedby the autonomous vehicle, cause the autonomous vehicle to: acquire, inresponse to detecting multiple obstacle laser point clouds in a currentlaser point cloud frame, multiple historical obstacle laser point cloudsequences, each historical obstacle laser point cloud in a samehistorical obstacle laser point cloud sequence representing a sameobstacle, each historical obstacle laser point cloud in the historicalobstacle laser point cloud sequence being an obstacle laser point cloudin a historical laser point cloud frame collected prior to collectingthe current laser point cloud frame; calculate a respective associationdegree between each detected obstacle laser point cloud in the currentlaser point cloud frame and each historical obstacle laser point cloudsequence based on association information to obtain multiple associationdegrees, the association information including: a similarity degreebetween an appearance characteristic of the detected obstacle laserpoint cloud in the current laser point cloud frame and an appearancecharacteristic of the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence, and consistency betweena current motion characteristic of an obstacle represented by thedetected obstacle laser point cloud in the current laser point cloudframe and a historical motion characteristic of an obstacle representedby the historical obstacle laser point cloud in the historical obstaclelaser point cloud sequence; and search for a historical obstacle laserpoint cloud sequence to which each detected obstacle laser point cloudin the current laser point cloud frame belongs based on the multipleassociation degrees.

It should be noted that the computer readable medium in the presentdisclosure may be computer readable signal medium or computer readablestorage medium or any combination of the above two. An example of thecomputer readable storage medium may include, but not limited to:electric, magnetic, optical, electromagnetic, infrared, or semiconductorsystems, apparatus, elements, or a combination any of the above. A morespecific example of the computer readable storage medium may include butis not limited to: electrical connection with one or more wire, aportable computer disk, a hard disk, a random access memory (RAM), aread only memory (ROM), an erasable programmable read only memory (EPROMor flash memory), a fibre, a portable compact disk read only memory(CD-ROM), an optical memory, a magnet memory or any suitable combinationof the above. In the present disclosure, the computer readable storagemedium may be any physical medium containing or storing programs whichcan be used by a command execution system, apparatus or element orincorporated thereto. In the present disclosure, the computer readablesignal medium may include data signal in the base band or propagating asparts of a carrier, in which computer readable program codes arecarried. The propagating signal may take various forms, including butnot limited to: an electromagnetic signal, an optical signal or anysuitable combination of the above. The signal medium that can be read bycomputer may be any computer readable medium except for the computerreadable storage medium. The computer readable medium is capable oftransmitting, propagating or transferring programs for use by, or usedin combination with, a command execution system, apparatus or element.The program codes contained on the computer readable medium may betransmitted with any suitable medium including but not limited to:wireless, wired, optical cable, RF medium etc., or any suitablecombination of the above.

The flow charts and block diagrams in the accompanying drawingsillustrate architectures, functions and operations that may beimplemented according to the systems, methods and computer programproducts of the various embodiments of the present disclosure. In thisregard, each of the blocks in the flow charts or block diagrams mayrepresent a module, a program segment, or a code portion, said module,program segment, or code portion comprising one or more executableinstructions for implementing specified logic functions. It should alsobe noted that, in some alternative implementations, the functionsdenoted by the blocks may occur in a sequence different from thesequences shown in the figures. For example, any two blocks presented insuccession may be executed, substantially in parallel, or they maysometimes be in a reverse sequence, depending on the function involved.It should also be noted that each block in the block diagrams and/orflow charts as well as a combination of blocks may be implemented usinga dedicated hardware-based system executing specified functions oroperations, or by a combination of a dedicated hardware and computerinstruction.

The units involved in the embodiments of the present disclosure may beimplemented by means of software or hardware. The described units mayalso be provided in a processor, for example, described as: a processor,comprising a detection unit, a calculation unit and a matching unit,where the names of these units do not in some cases constitute alimitation to such units themselves. For example, the detection unit mayalso be described as “a unit for acquiring, in response to detectingmultiple obstacle laser point clouds in a current laser point cloudframe, multiple historical obstacle laser point cloud sequences.”

The above description only provides an explanation of the preferredembodiments of the present disclosure and the technical principles used.It should be appreciated by those skilled in the art that the inventivescope of the present disclosure is not limited to the technicalsolutions formed by the particular combinations of the above-describedtechnical features. The inventive scope should also cover othertechnical solutions formed by any combinations of the above-describedtechnical features or equivalent features thereof without departing fromthe concept of the disclosure. Technical schemes formed by theabove-described features being interchanged with, but not limited to,technical features with similar functions disclosed in the presentdisclosure are example.

What is claimed is:
 1. A method for tracking an obstacle, comprising:acquiring, in response to detecting a plurality of obstacle laser pointclouds in a current laser point cloud frame, a plurality of historicalobstacle laser point cloud sequences, each historical obstacle laserpoint cloud in a same historical obstacle laser point cloud sequencerepresenting a same obstacle, each historical obstacle laser point cloudin the historical obstacle laser point cloud sequences being an obstaclelaser point cloud in a historical laser point cloud frame collectedprior to collecting the current laser point cloud frame; calculating arespective association degree between each detected obstacle laser pointcloud in the current laser point cloud frame and each historicalobstacle laser point cloud sequence based on association information toobtain a plurality of association degrees, the association informationcomprising: a similarity degree between an appearance characteristic ofthe detected obstacle laser point cloud in the current laser point cloudframe and an appearance characteristic of the historical obstacle laserpoint cloud in the historical obstacle laser point cloud sequence, andconsistency between a current motion characteristic of an obstaclerepresented by the detected obstacle laser point cloud in the currentlaser point cloud frame and a historical motion characteristic of anobstacle represented by the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence; and searching for ahistorical obstacle laser point cloud sequence to which each detectedobstacle laser point cloud in the current laser point cloud framebelongs based on the plurality of association degrees.
 2. The methodaccording to claim 1, wherein the appearance characteristic comprises: asize, a number of a laser point, a laser point density, and ageometrical shape.
 3. The method according to claim 2, wherein thecurrent motion characteristic of the obstacle represented by thedetected obstacle laser point cloud in the current laser point cloudframe comprises: an observed speed of the obstacle represented by theobstacle laser point cloud detected at a moment of collecting thecurrent laser point cloud frame, and an observed posture of the obstaclerepresented by the obstacle laser point cloud detected at the moment ofcollecting the current laser point cloud frame, and the historicalmotion characteristic of the obstacle represented by the historicalobstacle laser point cloud in the historical obstacle laser point cloudsequence comprises: a historical speed obtained through a motionestimation of the obstacle represented by the historical obstacle laserpoint cloud at a moment of collecting the historical laser point cloudframe to which the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence belongs, and a historicalposture of the obstacle represented by the historical obstacle laserpoint cloud at the moment of collecting the historical laser point cloudframe to which the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence belongs.
 4. The methodaccording to claim 3, wherein the searching for a historical obstaclelaser point cloud sequence to which each detected obstacle laser pointcloud in the current laser point cloud frame belongs based on theplurality of association degrees comprises: establishing a bipartitegraph, wherein the each detected obstacle laser point cloud in thecurrent laser point cloud frame and the each historical obstacle laserpoint cloud sequence in the plurality of historical obstacle laser pointcloud sequences correspond to respective nodes in the bipartite graph,and for a pair of the detected obstacle laser point cloud in the currentlaser point cloud frame and the historical obstacle laser point cloudsequence having an association degree greater than an association degreethreshold, a weight of a line segment between a node corresponding tothe detected obstacle laser point cloud in the current laser point cloudframe and a node corresponding to the historical obstacle laser pointcloud sequence in the bipartite graph is the association degree; andsearching for the historical obstacle laser point cloud sequence towhich each detected obstacle laser point cloud in the current laserpoint cloud frame belongs based on the bipartite graph.
 5. The methodaccording to claim 4, wherein the searching for the historical obstaclelaser point cloud sequence to which each detected obstacle laser pointcloud in the current laser point cloud frame belongs based on thebipartite graph comprises: dividing the bipartite graph into a pluralityof sub-bipartite graphs using a graph traversal method; and acquiring asolution for each of the sub-bipartite graphs by: establishing ahistorical obstacle laser point cloud sequence comprising the detectedobstacle laser point cloud in the current laser point cloud frame whenthe each sub-bipartite graph comprises a node representing the detectedobstacle laser point cloud in the current laser point cloud frame anddoes not comprise a node representing the historical obstacle laserpoint cloud sequence, updating a number of unmatched times of thehistorical obstacle laser point cloud sequence when the eachsub-bipartite graph comprises the node representing the historicalobstacle laser point cloud sequence and does not comprise the noderepresenting the detected obstacle laser point cloud in the currentlaser point cloud frame, and calculating a maximum match of the eachsub-bipartite graph using a Hungarian algorithm to obtain a matchingresult when the each sub-bipartite graph comprises the node representingthe detected obstacle laser point cloud in the current laser point cloudframe, and the node representing the historical obstacle laser pointcloud sequence, adding the detected obstacle laser point cloud in thecurrent laser point cloud frame in the matching result to the historicalobstacle laser point cloud sequence in the matching result, obtaining aspeed of the obstacle represented by the detected obstacle laser pointcloud through using a preset filtering algorithm to perform a motionestimation of the obstacle represented by the detected obstacle laserpoint cloud in the current laser point cloud frame in the matchingresult, and updating a number of unmatched times of the historicalobstacle laser point cloud sequence non-corresponding to the matchingresult.
 6. The method according to claim 5, further comprising:determining whether the plurality of historical obstacle laser pointcloud sequences comprises a historical obstacle laser point cloudsequence meeting at least one preset condition, the preset conditioncomprising: a number of unmatched times within a preset time lengthbeing greater than a number threshold, a ratio of the number ofunmatched times to a sum of a number of matching times and the number ofunmatched times within the preset time length being greater than a ratiothreshold, a number of consecutively unmatched times within the presettime length being greater than the number threshold, and a ratio of thenumber of consecutively unmatched times to the number of unmatched timeswithin the preset time length being greater than the ratio threshold;and deleting the historical obstacle laser point cloud sequence meetingthe at least one preset condition, when the plurality of historicalobstacle laser point cloud sequences comprises the historical obstaclelaser point cloud sequence meeting at least one preset condition.
 7. Anapparatus for tracking an obstacle, comprising: at least one processor;and a memory storing instructions, the instructions when executed by theat least one processor, cause the at least one processor to performoperations, the operations comprising: acquiring, in response todetecting a plurality of obstacle laser point clouds in a current laserpoint cloud frame, a plurality of historical obstacle laser point cloudsequences, each historical obstacle laser point cloud in a samehistorical obstacle laser point cloud sequence representing a sameobstacle, the each historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence being an obstacle laserpoint cloud in a historical laser point cloud frame collected prior tocollecting the current laser point cloud frame; calculating a respectiveassociation degree between each detected obstacle laser point cloud inthe current laser point cloud frame and each historical obstacle laserpoint cloud sequence based on association information to obtain aplurality of association degrees, the association informationcomprising: a similarity degree between an appearance characteristic ofthe detected obstacle laser point cloud in the current laser point cloudframe and an appearance characteristic of the historical obstacle laserpoint cloud in the historical obstacle laser point cloud sequence, andconsistency between a current motion characteristic of an obstaclerepresented by the detected obstacle laser point cloud in the currentlaser point cloud frame and a historical motion characteristic of anobstacle represented by the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence; and searching for ahistorical obstacle laser point cloud sequence to which each detectedobstacle laser point cloud in the current laser point cloud framebelongs based on the plurality of association degrees.
 8. The apparatusaccording to claim 7, wherein the appearance characteristic comprises: asize, a number of a laser point, a laser point density, and ageometrical shape.
 9. The apparatus according to claim 8, wherein thecurrent motion characteristic of the obstacle represented by thedetected obstacle laser point cloud in the current laser point cloudframe comprises: an observed speed of the obstacle represented by thedetected obstacle laser point cloud at a moment of collecting thecurrent laser point cloud frame, and an observed posture of the obstaclerepresented by the detected obstacle laser point cloud at the moment ofcollecting the current laser point cloud frame, and the historicalmotion characteristic of the obstacle represented by the historicalobstacle laser point cloud in the historical obstacle laser point cloudsequence comprises: a historical speed obtained through a motionestimation of the obstacle represented by the historical obstacle laserpoint cloud at a moment of collecting the historical laser point cloudframe to which the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence belongs, and a historicalposture of the obstacle represented by the historical obstacle laserpoint cloud at the moment of collecting the historical laser point cloudframe to which the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence belongs.
 10. Theapparatus according to claim 9, wherein the searching for a historicalobstacle laser point cloud sequence to which each detected obstaclelaser point cloud in the current laser point cloud frame belongs basedon the plurality of association degrees comprises: establishing abipartite graph, wherein the each detected obstacle laser point cloud inthe current laser point cloud frame and the each historical obstaclelaser point cloud sequence in the plurality of historical obstacle laserpoint cloud sequences correspond to respective nodes in the bipartitegraph, and for a pair of the detected obstacle laser point cloud in thecurrent laser point cloud frame and the historical obstacle laser pointcloud sequence having an association degree greater than an associationdegree threshold, a weight of a line segment between a nodecorresponding to the detected obstacle laser point cloud in the currentlaser point cloud frame and a node corresponding to the historicalobstacle laser point cloud sequence in the bipartite graph is theassociation degree; and searching for the historical obstacle laserpoint cloud sequence to which each detected obstacle laser point cloudin the current laser point cloud frame belongs based on the bipartitegraph.
 11. The apparatus according to claim 10, wherein the searchingfor the historical obstacle laser point cloud sequence to which eachdetected obstacle laser point cloud in the current laser point cloudframe belongs based on the bipartite graph comprises: dividing thebipartite graph into a plurality of sub-bipartite graphs using a graphtraversal method; and acquiring a solution for each of the sub-bipartitegraphs by: establishing a historical obstacle laser point cloud sequencecomprising the detected obstacle laser point cloud in the current laserpoint cloud frame when the each sub-bipartite graph comprises a noderepresenting the detected obstacle laser point cloud in the currentlaser point cloud frame and does not comprise a node representing thehistorical obstacle laser point cloud sequence; updating a number ofunmatched times of the historical obstacle laser point cloud sequencewhen the each sub-bipartite graph comprises the node representing thehistorical obstacle laser point cloud sequence and does not comprise thenode representing the detected obstacle laser point cloud in the currentlaser point cloud frame; and calculating a maximum match of the eachsub-bipartite graph using a Hungarian algorithm to obtain a matchingresult when the each sub-bipartite graph comprises the node representingthe detected obstacle laser point cloud in the current laser point cloudframe, and the node representing the historical obstacle laser pointcloud sequence, adding the detected obstacle laser point cloud in thecurrent laser point cloud frame in the matching result to the historicalobstacle laser point cloud sequence in the matching result, obtaining aspeed of the obstacle represented by the detected obstacle laser pointcloud through using a preset filtering algorithm to perform a motionestimation of the obstacle represented by the detected obstacle laserpoint cloud in the current laser point cloud frame in the matchingresult, and updating a number of unmatched times of the historicalobstacle laser point cloud sequence non-corresponding to correspondingmatching result.
 12. The apparatus according to claim 11, the operationsfurther comprising: determining whether the plurality of historicalobstacle laser point cloud sequences comprises a historical obstaclelaser point cloud sequence meeting at least one preset condition, thepreset condition comprising: a number of unmatched times within a presettime length being greater than a number threshold, a ratio of the numberof unmatched times to a sum of a number of matching times and the numberof unmatched times within the preset time length being greater than aratio threshold, a number of consecutively unmatched times within thepreset time length being greater than the number threshold, and a ratioof the number of consecutively unmatched times to the number ofunmatched times within the preset time length being greater than theratio threshold; and deleting the historical obstacle laser point cloudsequence meeting the at least one preset condition, when the pluralityof historical obstacle laser point cloud sequences comprises thehistorical obstacle laser point cloud sequence meeting at least onepreset condition.
 13. A non-transitory computer medium, storing acomputer program, wherein the program, when executed by a processor,causes the processor to perform operations, the operations comprising:acquiring, in response to detecting a plurality of obstacle laser pointclouds in a current laser point cloud frame, a plurality of historicalobstacle laser point cloud sequences, each historical obstacle laserpoint cloud in a same historical obstacle laser point cloud sequencerepresenting a same obstacle, each historical obstacle laser point cloudin the historical obstacle laser point cloud sequences being an obstaclelaser point cloud in a historical laser point cloud frame collectedprior to collecting the current laser point cloud frame; calculating arespective association degree between each detected obstacle laser pointcloud in the current laser point cloud frame and each historicalobstacle laser point cloud sequence based on association information toobtain a plurality of association degrees, the association informationcomprising: a similarity degree between an appearance characteristic ofthe detected obstacle laser point cloud in the current laser point cloudframe and an appearance characteristic of the historical obstacle laserpoint cloud in the historical obstacle laser point cloud sequence, andconsistency between a current motion characteristic of an obstaclerepresented by the detected obstacle laser point cloud in the currentlaser point cloud frame and a historical motion characteristic of anobstacle represented by the historical obstacle laser point cloud in thehistorical obstacle laser point cloud sequence; and searching for ahistorical obstacle laser point cloud sequence to which each detectedobstacle laser point cloud in the current laser point cloud framebelongs based on the plurality of association degrees.